Att-Net: Enhanced emotion recognition system using lightweight self-attention module
S Kwon - Applied Soft Computing, 2021 - Elsevier
Speech emotion recognition (SER) is an active research field of digital signal processing
and plays a crucial role in numerous applications of Human–computer interaction (HCI) …
and plays a crucial role in numerous applications of Human–computer interaction (HCI) …
Exploring automatic COVID-19 diagnosis via voice and symptoms from crowdsourced data
The development of fast and accurate screening tools, which could facilitate testing and
prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context …
prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context …
Multi-task semi-supervised adversarial autoencoding for speech emotion recognition
Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art
accuracy is quite low and needs improvement to make commercial applications of SER …
accuracy is quite low and needs improvement to make commercial applications of SER …
A systematic review on automated clinical depression diagnosis
Assessing mental health disorders and determining treatment can be difficult for a number of
reasons, including access to healthcare providers. Assessments and treatments may not be …
reasons, including access to healthcare providers. Assessments and treatments may not be …
Graphcfc: A directed graph based cross-modal feature complementation approach for multimodal conversational emotion recognition
Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer
Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can …
Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can …
Exploiting vocal tract coordination using dilated cnns for depression detection in naturalistic environments
Depression detection from speech continues to attract significant research attention but
remains a major challenge, particularly when the speech is acquired from diverse …
remains a major challenge, particularly when the speech is acquired from diverse …
Multi-channel weight-sharing autoencoder based on cascade multi-head attention for multimodal emotion recognition
Multimodal Emotion Recognition is challenging because of the heterogeneity gap among
different modalities. Due to the powerful ability of feature abstraction, Deep Neural Networks …
different modalities. Due to the powerful ability of feature abstraction, Deep Neural Networks …
Investigation of speech landmark patterns for depression detection
The massive and growing burden imposed on modern society by depression has motivated
investigations into early detection through automated, scalable and non-invasive methods …
investigations into early detection through automated, scalable and non-invasive methods …
Natural language processing methods for acoustic and landmark event-based features in speech-based depression detection
The processing of speech as an explicit sequence of events is common in automatic speech
recognition (linguistic events), but has received relatively little attention in paralinguistic …
recognition (linguistic events), but has received relatively little attention in paralinguistic …
[PDF][PDF] Domain adaptation for enhancing Speech-based depression detection in natural environmental conditions using dilated CNNs.
Depression disorders are a major growing concern worldwide, especially given the unmet
need for widely deployable depression screening for use in real-world environments …
need for widely deployable depression screening for use in real-world environments …